ATOMDANCE: Kernel-based denoising and choreographic analysis for protein dynamic comparison.

Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.

Evolution of drug resistance drives destabilization of flap region dynamics in HIV-1 protease.

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome. During the progression of the disease, some individual patients acquire drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the "flap region" of the HIV-1 protease, which is a critical dynamic region involved in nonspecific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning-assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and drug binding of wild-type and common drug-resistant versions of the main protease. We also use a multiagent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs. We find that a key conserved functional site in the flap region, a solvent-exposed isoleucine (Ile50) that controls flap dynamics is functionally targeted by drug resistance mutations, leading to amplified molecular dynamics affecting the functional ability of the flap region to hold the drugs. We conclude that better long-term patient outcomes may be achieved by designing drugs that target protease regions that are less dependent upon single sites with large functional binding effects.

Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation.

Widespread human transmission of SARS-CoV-2 highlights the substantial public health, economic and societal consequences of virus spillover from wildlife and also presents a repeated risk of reverse spillovers back to naive wildlife populations. We employ comparative statistical analyses of a large set of short-term molecular dynamic (MD) simulations to investigate the potential human-to-bat (genus Rhinolophus) cross-species infectivity allowed by the binding of SARS-CoV-2 receptor-binding domain (RBD) to angiotensin-converting enzyme 2 (ACE2) across the bat progenitor strain and emerging human strain variants of concern (VOC). We statistically compare the dampening of atom motion across protein sites upon the formation of the RBD/ACE2 binding interface using various bat versus human target receptors (i.e. bACE2 and hACE2). We report that while the bat progenitor viral strain RaTG13 shows some pre-adaption binding to hACE2, it also exhibits stronger affinity to bACE2. While early emergent human strains and later VOCs exhibit robust binding to both hACE2 and bACE2, the delta and omicron variants exhibit evolutionary adaption of binding to hACE2. However, we conclude there is a still significant risk of mammalian cross-species infectivity of human VOCs during upcoming waves of infection as COVID-19 transitions from a pandemic to endemic status.

Identifying vaccine escape sites via statistical comparisons of short-term molecular dynamics.

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamics (MD)-based approach that goes beyond contact mapping, scales well to a desktop computer with a modern graphics processor, and enables the user to identify functional protein sites that are prone to vaccine escape in a viral antigen. We first implement our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs thus enabling a statistical comparison of the rapid motion of influenza hemagglutinin (HA) in both the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in influenza HA, predetermined via selection experiments and neutralization assays. Upon the validation of our computational approach, we then surveyed potential hotspot residues in the receptor binding domain of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified many single sites in the antigen-antibody interface that are similarly prone to potential antibody escape and that match many of the known sites of mutations arising in the SARS-CoV-2 variants of concern. In the Omicron variant, we find only minimal adaptive evolutionary shifts in the functional binding profiles of both antibodies. In summary, we provide an inexpensive and accurate computational method to monitor hotspots of functional evolution in antibody binding footprints.

Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline.

Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional protein dynamics (i.e., single unbound state vs. complex with a ligand, DNA, or protein). We utilize multi-agent machine learning classifiers to identify functionally conserved dynamic motions and compare them in genetic or drug-class variants. For complete details on the use and execution of this profile, please refer to Babbitt et al. (2020b, 2020a, 2018) and Rynkiewicz et al. (2021).

Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition.

Oncogenic mutations in the kinase domain of the B-Raf protein have long been associated with cancers involving the MAPK pathway. One constitutive MAPK activating mutation in B-Raf, the V600E (valine to glutamate) replacement occurring adjacent to a site of threonine phosphorylation (T599) occurs in many types of cancer, and in a large percentage of certain cancers, such as melanoma. Because ATP binding activity and the V600E mutation are both known to alter the physical behavior of the activation loop in the B-Raf ATP binding domain, this system is especially amenable to comparative analyses of molecular dynamics simulations modeling various genetic and drug class variants. Here, we employ machine learning enabled identification of functionally conserved protein dynamics to compare how the binding interactions of four B-Raf inhibitors impact the functional loop dynamics controlling ATP activation. We demonstrate that drug development targeting B-Raf has progressively moved towards ATP competitive inhibitors that demonstrate less tendency to mimic the functionally conserved dynamic changes associated with ATP activation and leading to the side effect of hyperactivation (i.e. inducing MAPK activation in non-tumorous cells in the absence of secondary mutation). We compare the functional dynamic impacts of V600E and other sensitizing and drug resistance causing mutations in the regulatory loops of B-Raf, confirming sites of low mutational tolerance in these regions. Lastly, we investigate V600E sensitivity of B-Raf loop dynamics in an evolutionary context, demonstrating that while sensitivity has an ancient origin with primitive eukaryotes, it was also secondarily increased during early jawed vertebrate evolution.Communicated by Ramaswamy H. Sarma.

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains.

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. A multi-agent classifier successfully identified functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing residues K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain's interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.Communicated by Ramaswamy H. Sarma.

Behavioral impacts on residential food provisioning, use, and waste during the COVID-19 pandemic.

The COVID-19 pandemic caused unprecedented disruptions to food systems, leading to both food shortages and food waste across the supply chain. These disruptions have, in turn, altered how people consume and then ultimately discard food. To better understand these impacts, their underlying drivers, and their sustainability implications, this study surveyed U.S. consumers about food purchasing, use, and waste behaviors during the pandemic. Survey respondents reported an increase in overall food purchases and a slight decrease in food waste generation due to the pandemic, but the linkages between these outcomes and underlying behaviors were complex. For instance, reduced household food waste was significantly correlated with an increase in behaviors such as meal planning, preserving foods, and using leftovers and shelf-stable items. On the other hand, behaviors aimed at self-sufficiency, including bulk purchasing and stockpiling, were significantly correlated with increased food purchase, which in turn led to increased waste. Results may offer insight for future resource and waste management strategies. For example, over 60% of respondents who started or increased efficient food use behaviors stated an intent to continue these activities after the pandemic. In contrast, less than 10% of respondents reported that they began or increased separating or composting food waste during the pandemic, and many stopped altogether due to suspension of local curbside composting services. Findings suggest that it may be easier to shift food consumption and use behaviors but more challenging to alter food waste separation behaviors, particularly those influenced by external factors, such as infrastructure that may be vulnerable to disruption. Identifying ways to facilitate ongoing behavior change and foster robust food waste management systems can contribute to resilience of food systems now and once the immediate threat of the pandemic has subsided.

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains.

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain's interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

Comparative Molecular Dynamics Simulations Provide Insight Into Antibiotic Interactions: A Case Study Using the Enzyme L,L-Diaminopimelate Aminotransferase (DapL).

The L,L-diaminopimelate aminotransferase (DapL) pathway, a recently discovered variant of the lysine biosynthetic pathway, is an attractive pipeline to identify targets for the development of novel antibiotic compounds. DapL is a homodimer that catalyzes the conversion of tetrahydrodipicolinate to L,L-diaminopimelate in a single transamination reaction. The penultimate and ultimate products of the lysine biosynthesis pathway, meso-diaminopimelate and lysine, are key components of the Gram-negative and Gram-positive bacterial peptidoglycan cell wall. Humans are not able to synthesize lysine, and DapL has been identified in 13% of bacteria whose genomes have been sequenced and annotated to date, thus it is an attractive target for the development of narrow spectrum antibiotics through the prevention of both lysine biosynthesis and peptidoglycan crosslinking. To address the common lack of structural information when conducting compound screening experiments and provide support for the use of modeled structures, our analyses utilized inferred structures from related homologous enzymes. Using a comprehensive and comparative molecular dynamics (MD) software package-DROIDS (Detecting Relative Outlier Impacts in Dynamic Simulations) 2.0, we investigated the binding dynamics of four previously identified antagonistic ligands of DapL from Verrucomicrobium spinosum, a non-pathogenic relative of Chlamydia trachomatis. Here, we present putative docking positions of the four ligands and provide confirmatory comparative molecular dynamics simulations supporting the conformations. The simulations performed in this study can be applied to evaluate putative targets to predict compound effectiveness prior to in vivo and in vitro experimentation. Moreover, this approach has the potential to streamline the process of antibiotic development.

DROIDS 3.0-Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics.

The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.

DROIDS 1.20: A GUI-Based Pipeline for GPU-Accelerated Comparative Protein Dynamics.

Traditional informatics in comparative genomics work only with static representations of biomolecules (i.e., sequence and structure), thereby ignoring the molecular dynamics (MD) of proteins that define function in the cell. A comparative approach applied to MD would connect this very short timescale process, defined in femtoseconds, to one of the longest in the universe: molecular evolution measured in millions of years. Here, we leverage advances in graphics-processing-unit-accelerated MD simulation software to develop a comparative method of MD analysis and visualization that can be applied to any two homologous Protein Data Bank structures. Our open-source pipeline, DROIDS (Detecting Relative Outlier Impacts in Dynamic Simulations), works in conjunction with existing molecular modeling software to convert any Linux gaming personal computer into a "comparative computational microscope" for observing the biophysical effects of mutations and other chemical changes in proteins. DROIDS implements structural alignment and Benjamini-Hochberg-corrected Kolmogorov-Smirnov statistics to compare nanosecond-scale atom bond fluctuations on the protein backbone, color mapping the significant differences identified in protein MD with single-amino-acid resolution. DROIDS is simple to use, incorporating graphical user interface control for Amber16 MD simulations, cpptraj analysis, and the final statistical and visual representations in R graphics and UCSF Chimera. We demonstrate that DROIDS can be utilized to visually investigate molecular evolution and disease-related functional changes in MD due to genetic mutation and epigenetic modification. DROIDS can also be used to potentially investigate binding interactions of pharmaceuticals, toxins, or other biomolecules in a functional evolutionary context as well.

Triplet-Based Codon Organization Optimizes the Impact of Synonymous Mutation on Nucleic Acid Molecular Dynamics.

Since the elucidation of the genetic code almost 50 years ago, many nonrandom aspects of its codon organization remain only partly resolved. Here, we investigate the recent hypothesis of 'dual-use' codons which proposes that in addition to allowing adjustment of codon optimization to tRNA abundance, the degeneracy in the triplet-based genetic code also multiplexes information regarding DNA's helical shape and protein-binding dynamics while avoiding interference with other protein-level characteristics determined by amino acid properties. How such structural optimization of the code within eukaryotic chromatin could have arisen from an RNA world is a mystery, but would imply some preadaptation in an RNA context. We analyzed synonymous (protein-silent) and nonsynonymous (protein-altering) mutational impacts on molecular dynamics in 13823 identically degenerate alternative codon reorganizations, defined by codon transitions in 7680 GPU-accelerated molecular dynamic simulations of implicitly and explicitly solvated double-stranded aRNA and bDNA structures. When compared to all possible alternative codon assignments, the standard genetic code minimized the impact of synonymous mutations on the random atomic fluctuations and correlations of carbon backbone vector trajectories while facilitating the specific movements that contribute to DNA polymer flexibility. This trend was notably stronger in the context of RNA supporting the idea that dual-use codon optimization and informational multiplexing in DNA resulted from the preadaptation of the RNA duplex to resist changes to thermostability. The nonrandom and divergent molecular dynamics of synonymous mutations also imply that the triplet-based code may have resulted from adaptive functional expansion enabling a primordial doublet code to multiplex gene regulatory information via the shape and charge of the minor groove.

Can all heritable biology really be reduced to a single dimension?

A long-held presupposition in the field of bioinformatics holds that genetic, and now even epigenetic 'information' can be abstracted from the physicochemical details of the macromolecular polymers in which it resides. It is perhaps rather ironic that this basic conjecture originated upon the first observations of DNA structure itself. This static model of DNA led very quickly to the conclusion that only the nucleobase sequence itself is rich enough in molecular complexity to replicate a complex biology. This idea has been pervasive throughout genomic science, higher education and popular culture ever since; to the point that most of us would accept it unquestioningly as fact. What is more alarming is that this conjecture is driving a significant portion of the technological development in modern genomics towards methods strongly rooted in DNA sequencing, thereby reducing a dynamic multi-dimensional biology into single-dimensional forms of data. Evidence countering this central tenet of bioinformatics has been quietly mounting over many decades, prompting some to propose that the genome must be studied from the perspective of its molecular reality, rather than as a body of information to be represented symbolically. Here, we explore the epistemological boundary between bioinformatics and molecular biology, and warn against an 'overtly' bioinformatic perspective. We review a selection of new bioinformatic methods that move beyond sequence-based approaches to include consideration of databased three dimensional structures. However, we also note that these hybrid methods still ignore the most important element of gene function when attempting to improve outcomes; the fourth dimension of molecular dynamics over time.

TRX-LOGOS - a graphical tool to demonstrate DNA information content dependent upon backbone dynamics in addition to base sequence.

BACKGROUND: It is now widely-accepted that DNA sequences defining DNA-protein interactions functionally depend upon local biophysical features of DNA backbone that are important in defining sites of binding interaction in the genome (e.g. DNA shape, charge and intrinsic dynamics). However, these physical features of DNA polymer are not directly apparent when analyzing and viewing Shannon information content calculated at single nucleobases in a traditional sequence logo plot. Thus, sequence logos plots are severely limited in that they convey no explicit information regarding the structural dynamics of DNA backbone, a feature often critical to binding specificity. SOFTWARE AND IMPLEMENTATION: We present TRX-LOGOS, an R software package and Perl wrapper code that interfaces the JASPAR database for computational regulatory genomics. TRX-LOGOS extends the traditional sequence logo plot to include Shannon information content calculated with regard to the dinucleotide-based BI-BII conformation shifts in phosphate linkages on the DNA backbone, thereby adding a visual measure of intrinsic DNA flexibility that can be critical for many DNA-protein interactions. TRX-LOGOS is available as an R graphics module offered at both SourceForge and as a download supplement at this journal. RESULTS: To demonstrate the general utility of TRX logo plots, we first calculated the information content for 416 Saccharomyces cerevisiae transcription factor binding sites functionally confirmed in the Yeastract database and matched to previously published yeast genomic alignments. We discovered that flanking regions contain significantly elevated information content at phosphate linkages than can be observed at nucleobases. We also examined broader transcription factor classifications defined by the JASPAR database, and discovered that many general signatures of transcription factor binding are locally more information rich at the level of DNA backbone dynamics than nucleobase sequence. We used TRX-logos in combination with MEGA 6.0 software for molecular evolutionary genetics analysis to visually compare the human Forkhead box/FOX protein evolution to its binding site evolution. We also compared the DNA binding signatures of human TP53 tumor suppressor determined by two different laboratory methods (SELEX and ChIP-seq). Further analysis of the entire yeast genome, center aligned at the start codon, also revealed a distinct sequence-independent 3 bp periodic pattern in information content, present only in coding region, and perhaps indicative of the non-random organization of the genetic code. CONCLUSION: TRX-LOGOS is useful in any situation in which important information content in DNA can be better visualized at the positions of phosphate linkages (i.e. dinucleotides) where the dynamic properties of the DNA backbone functions to facilitate DNA-protein interaction.

Synonymous codon bias and functional constraint on GC3-related DNA backbone dynamics in the prokaryotic nucleoid.

While mRNA stability has been demonstrated to control rates of translation, generating both global and local synonymous codon biases in many unicellular organisms, this explanation cannot adequately explain why codon bias strongly tracks neighboring intergene GC content; suggesting that structural dynamics of DNA might also influence codon choice. Because minor groove width is highly governed by 3-base periodicity in GC, the existence of triplet-based codons might imply a functional role for the optimization of local DNA molecular dynamics via GC content at synonymous sites (≈GC3). We confirm a strong association between GC3-related intrinsic DNA flexibility and codon bias across 24 different prokaryotic multiple whole-genome alignments. We develop a novel test of natural selection targeting synonymous sites and demonstrate that GC3-related DNA backbone dynamics have been subject to moderate selective pressure, perhaps contributing to our observation that many genes possess extreme DNA backbone dynamics for their given protein space. This dual function of codons may impose universal functional constraints affecting the evolution of synonymous and non-synonymous sites. We propose that synonymous sites may have evolved as an 'accessory' during an early expansion of a primordial genetic code, allowing for multiplexed protein coding and structural dynamic information within the same molecular context.

Functional conservation of nucleosome formation selectively biases presumably neutral molecular variation in yeast genomes.

One prominent pattern of mutational frequency, long appreciated in comparative genomics, is the bias of purine/pyrimidine conserving substitutions (transitions) over purine/pyrimidine altering substitutions (transversions). Traditionally, this transitional bias has been thought to be driven by the underlying rates of DNA mutation and/or repair. However, recent sequencing studies of mutation accumulation lines in model organisms demonstrate that substitutions generally do not accumulate at rates that would indicate a transitional bias. These observations have called into question a very basic assumption of molecular evolution; that naturally occurring patterns of molecular variation in noncoding regions accurately reflect the underlying processes of randomly accumulating neutral mutation in nuclear genomes. Here, in Saccharomyces yeasts, we report a very strong inverse association (r = -0.951, P < 0.004) between the genome-wide frequency of substitutions and their average energetic effect on nucleosome formation, as predicted by a structurally based energy model of DNA deformation around the nucleosome core. We find that transitions occurring at sites positioned nearest the nucleosome surface, which are believed to function most importantly in nucleosome formation, alter the deformation energy of DNA to the nucleosome core by only a fraction of the energy changes typical of most transversions. When we examined the same substitutions set against random background sequences as well as an existing study reporting substitutions arising in mutation accumulation lines of Saccharomyces cerevisiae, we failed to find a similar relationship. These results support the idea that natural selection acting to functionally conserve chromatin organization may contribute significantly to genome-wide transitional bias, even in noncoding regions. Because nucleosome core structure is highly conserved across eukaryotes, our observations may also help to further explain locally elevated transition bias at CpG islands, which are known to destabilize nucleosomes at vertebrate promoters.

The enrichment of TATA box and the scarcity of depleted proximal nucleosome in the promoters of duplicated yeast genes.

Population genetic theory of gene duplication suggests that the preservation of duplicate copies requires functional divergence upon duplication. Genes that can be readily modified to produce new gene expression patterns may thus be duplicated often. In yeast, genes exhibit dichotomous expression patterns based on their promoter architectures. The expression of genes that contain TATA box or occupied proximal nucleosome (OPN) tends to be variable and respond to external signals. On the other hand, genes without TATA box or with depleted proximal nucleosome (DPN) are expressed constitutively. We find that recent duplicates in the yeast genome are heavily biased to be TATA box containing genes and not to be DPN genes. This suggests that variably expressed genes, due to the functional organization in their promoters, have higher duplicability than constitutively expressed genes.

Evolution of product lifespan and implications for environmental assessment and management: a case study of personal computers in higher education.

Product lifespan is a fundamental variable in understanding the environmental impacts associated with the life cycle of products. Existing life cycle and materials flow studies of products, almost without exception, consider lifespan to be constant over time. To determine the validity of this assumption, this study provides an empirical documentation of the long-term evolution of personal computer lifespan, using a major U.S. university as a case study. Results indicate that over the period 1985-2000, computer lifespan (purchase to "disposal") decreased steadily from a mean of 10.7 years in 1985 to 5.5 years in 2000. The distribution of lifespan also evolved, becoming narrower over time. Overall, however, lifespan distribution was broader than normally considered in life cycle assessments or materials flow forecasts of electronic waste management for policy. We argue that these results suggest that at least for computers, the assumption of constant lifespan is problematic and that it is important to work toward understanding the dynamics of use patterns. We modify an age-structured model of population dynamics from biology as a modeling approach to describe product life cycles. Lastly, the purchase share and generation of obsolete computers from the higher education sector is estimated using different scenarios for the dynamics of product lifespan.

How accurate is the phenotype? - an analysis of developmental noise in a cotton aphid clone.

BACKGROUND: The accuracy by which phenotype can be reproduced by genotype potentially is important in determining the stability, environmental sensitivity, and evolvability of morphology and other phenotypic traits. Because two sides of an individual represent independent development of the phenotype under identical genetic and environmental conditions, average body asymmetry (or "fluctuating asymmetry") can estimate the developmental instability of the population. The component of developmental instability not explained by intrapopulational differences in gene or environment (or their interaction) can be further defined as internal developmental noise. Surprisingly, developmental noise remains largely unexplored despite its potential influence on our interpretations of developmental stability, canalization, and evolvability. Proponents of fluctuating asymmetry as a bioindicator of environmental or genetic stress, often make the assumption that developmental noise is minimal and, therefore, that phenotype can respond sensitively to the environment. However, biologists still have not measured whether developmental noise actually comprises a significant fraction of the overall environmental response of fluctuating asymmetry observed within a population. RESULTS: In a morphometric study designed to partition developmental noise from fluctuating asymmetry in the wing morphology of a monoclonal culture of cotton aphid, Aphis gosspyii, it was discovered that fluctuating asymmetry in the aphid wing was nearly four times higher than in other insect species. Also, developmental noise comprised a surprisingly large fraction ( approximately 50%) of the overall response of fluctuating asymmetry to a controlled graded temperature environment. Fluctuating asymmetry also correlated negatively with temperature, indicating that environmentally-stimulated changes in developmental instability are mediated mostly by changes in the development time of individuals. CONCLUSION: The amount of developmental noise revealed in this trait potentially does interfere with a substantial amount of the sensitivity of fluctuating asymmetry to change in temperature. Assuming that some genetic-based variation in individual buffering of developmental instability exists in natural aphid populations, the amount of internal developmental noise determined in this study could also substantially reduce evolvability of the aphid wing. The overall findings here suggest that individual response to the seemingly high cost of stabilizing some aspects of the phenotype may account for the frequent observation of trait and species specificity in levels of fluctuating asymmetry.